Image recognition technology has been widely used in daily life. For example, information on a number plate of a passing vehicle may be recognized by using the image recognition technology. The image recognition technology often needs to convert a non-digital image into a digital image, and then recognizes the digital image. Alternatively, the image recognition technology directly obtains a digital image and recognizes the digital image. However, the information obtained by recognizing the image by using the existing image recognition technology is insufficient, and more image information cannot be given. In the above example that the image recognition technology is used for recognizing the information on a number plate, the existing image recognition technology may recognize an image of the number plate, but the detailed information (e.g., numbers, characters on the number plate) of the image of the number plate needs to be manually recognized.
A method for recognizing the granularity of an image is a method to establish an association between an image and a semantic label and describing the image by using the semantic label with the association. The granularity refers to further sub-class recognition of digital image content on the basis of recognizing the content type of the digital image. A semantic label is used for literally illustrating the digital image. For example, for an image containing a puppy, the existing image recognition technology may recognize only the image of the puppy, but cannot provide more information of the puppy. The image granularity recognition method may be used for recognizing not only the image of the puppy, but also the detailed information of the puppy, for example, the breed and color (i.e. granularity information), and outputting the detailed information of the puppy in the form of a semantic label. It should be noted that the granularity is a relative concept, and the meaning of the granularity may be different, for different digital images or image contents.
The image granularity recognition process of the existing image granularity recognition method is as follows. An image feature of the entire image, or a local feature of the image in a manually pre-selected image area is extracted first. Then, a semantic label is set for the image feature or the local feature. Since the semantic label is obtained based on the extracted image feature or the local feature obtained by manually selecting an image area, no accurate semantic label may be provided and it is difficult to apply the method widely.